Global status of policies and practices for systematic TB screening in high-burden countries
Bibliographic record
Abstract
BACKGROUND: The global status of policies and practices for systematic TB screening has not been described since the World Health Organization (WHO) guideline update in 2021. In 2024, the WHO Global Programme on Tuberculosis & Lung Health commissioned a questionnaire survey and in-depth reviews of systematic screening for TB disease in high-TB-burden countries. METHODS: A short-answer and multiple-choice questionnaire was sent to the 30 highest-TB-burden countries to query national policies and the scale of systematic screening, prioritising practices and results among targeted populations. In eight of the 30 countries, mixed-methods in-depth reviews comprised national policy desk reviews; stakeholder interviews; subnational site visits; and analyses of TB cascade of care data from systematic TB screening (2021-2023). RESULTS: Systematic TB screening showed signs of expansion since 2021 in eight high-TB-burden countries. The questionnaire survey and in-depth reviews identified best practices including chest X-ray prioritisation in parallel with or replacing symptom screening, computer-aided detection for chest X-ray interpretation, machine-learning spatial analytics, and intensive community mobilisation. CONCLUSION: High-TB-burden countries have expanded systematic TB screening, but national data systems for monitoring and evaluation must be strengthened to evaluate systematic screening results. Funding and human resource mobilisation is critical for progress. Artificial intelligence and innovative tools may improve implementation quality, enhancing existing human workforce capacity in the context of limited resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.117 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".